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test<-read.csv("D:\allGroups\Females_Mated\Assa_Female_Mated_Unknown_RigA_20211025T120646\all_frames_degree.csv")

Error: '\F' is an unrecognized escape in character string starting ""D:\allGroups\F"

#creat graph for one movie in perframe

test<-read.csv("D:/allGroups/Females_Mated/Assa_Female_Mated_Unknown_RigA_20211025T120646/all_frames_degree.csv")


all_features<-read.csv("D:/allGroups/Females_Mated/Assa_Female_Mated_Unknown_RigA_20211025T120646/perframe_sum_allflies.csv")
sum_per_frames<-test[,c(1,3)]
colnames(sum_per_frames)<-c("Frame","Value")

#title
title_of_plot<-paste0(basename(path)," plot of dgree in each ",number_of_frame," frames_without angelsub")

all_features_new<-all_features[1:27000,]

data<-cbind(all_features_new,sum_per_frames$Value)

data_cor <- cor(data[ , colnames(data) != "Value"],  # Calculate correlations
                data$Value)
data_cor  
library(corrplot)
corrplot(data_cor, method="circle",tl.cex = 0.5)
corrplot(data_cor, type="upper", order="hclust", tl.col="black", tl.srt=45,tl.cex = 0.4)


#png(filename = "mycorrplot.png", width = 1200, height = 800)
corrplot(data_cor, method="circle", addCoef.col = 1, tl.cex = 0.5)
#dev.off()

  library(ggplot2)

p<-ggplot()+geom_line(data = sum_per_frames, mapping = aes(x = Frame, y = Value))
plot(p)


 

correlaction

install.packages("heatmaply")

Error in install.packages : Updating loaded packages

library(heatmaply)

# plotting corr heatmap
heatmaply_cor(x = cor(data), xlab = "Features",
            ylab = "Features", k_col = 2, k_row = 2)

install.packages("heatmaply")

WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/

Warning in install.packages :
  package ‘heatmaply’ is in use and will not be installed

library(lubridate)


my_t = ts(data[,1:2])

my_t = ts(s, start = c(2019,1), frequency = 19)


plot(aggregate(my_t), type="p", pch=20, cex=3, ylab="Aggregate per time unit")

set.seed(9797)
s_1 = seq(221:256)

# A cyclic vector for SEASONAL variations.
s_2 = cos(seq(from=1/12*pi, to=6*pi, by=(6*pi/36)))*5

# Random numbers for errors (white noise).
set.seed(65)
s_3 = rnorm(36)

# The data vector.
s = s_1 + s_2 + s_3

# The time series object is created.
my_t = ts(s, start = c(2019,1), frequency = 12)

# Plot the time series.
plot(my_t, ylab = "Values")

xts(data$Value, as.Date(data$Frame))



 library(zoo)
 #ZOO <- zoo(data$Value, as.character(data$Frame))
 ZOO <- zoo(data$Value)

 my_t<-ts(ZOO,frequency = 2700)
 plot(my_t)
 
 
plot(aggregate(my_t), type="p", pch=20, cex=3, ylab="Aggregate per time unit")

library(lubridate)

library(forecast)

my_vec<-as.vector(data$Value)
myts <- ts(my_vec, start=c(1, 1), frequency=2700)


 plot(myts)
 fit <- stl(myts, s.window="period")

plot(fit)

fit <- auto.arima(my_t)
forecast(fit, 20)


plot(forecast(fit, 2000))